QuoteR

Official code and data of the ACL 2022 paper "QuoteR: A Benchmark of Quote Recommendation for Writing" [pdf]

1. Requirements

• nltk==3.5
• numpy==1.19.5
• sklearn==0.0
• torch==1.7.1+cu110
• transformers==3.0.2
• OpenHowNet==0.0.1a11

2. Usage

2.1 Generate sememe data

english_word_sememe.py and chinese_token_sememe.py are used to generate the corresponding sememe data for English and Chinese, respectively.

2.2 Modify files in the Transformer Python library

Modify the modeling_bert.py file in the Transformer Python library and add three Classes (SememeEmbeddings, BertSememeEmbeddings, BertSememeModel) in bert_chinese_sememe.py or bert_english_sememe.py.

Note that change the path of the corresponding sememe data in the SememeEmbeddings Class. Then add the BertSemeModel Class to the __init__.py file in the Transformer Python library.

2.3 Model training and testing

The files english_train_test.py, modern_chinese_train_test.py and ancient_chinese_train_test.py were used for training and testing, respectively.

Cite

If the code or data help you, please cite the following paper.

@inproceedings{qi2022quoter,
  title={QuoteR: A Benchmark of Quote Recommendation for Writing},
  author={Qi, Fanchao and Yang, Yanhui and Yi, Jing and Cheng, Zhili and Liu, Zhiyuan and Sun, Maosong},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  url = {https://aclanthology.org/2022.acl-long.27},
  pages={336--348},
  year={2022}
}